1. Dominic Adorno and Michael Salvatore Optimizing Requester Deployment

2. Organ Donation Impact of increase in authorization rate Refusal – largest impediment

3. Background Literature review “Thought” leaders Case circumstances Death factors Perceived quality of care Requester demographics Timing (request, donation discussion, brain death) Location (region and hospital unit) Decoupling of death and donation

4. Pre-Study Era Development of Family Metrics Tool used for performance monitoring and data mining Monthly data reviews with Manager of Family Services Global and coordinator authorization rates eligibility, ethnicity, region, hospital, etc.

5. Family Metrics Tool

6. Study Development Anecdotal evidence suggests that coordinator’s performance varies based on case circumstances Create a methodology for requester deployment that utilizes historical outcomes to make staffing recommendations for future donation discussions

7. Machine Learning Pattern recognition/ algorithm development Recommendation engines Amazon Autocomplete (Google, texting)

8. Amazon Analogy Disparate sets Exogenous factors (customers, case circumstances) Endogenous factors (products, requesters) Past relationships (purchase history, authorization outcomes) Goal Matching endogenous with exogenous to achieve best outcomes

9. Machine Learning Cont’d 3 years of data (2012-2014) Random Forest Modeling Variable importance charts Impact on authorization (yes/no) FPA excluded

10. Machine Learning Cont’d Training Data Set 2 years of data (2012-2013) Pattern recognition (requesters and case circumstances) Validation Data Set Probability scoring applied to 2014 data >0.5 = Yes, <0.5 = No 76% accuracy (actual outcome vs. predicted outcome) Gap assessment (requester chosen vs. requester with highest probability) ~21% authorization rate

11. Machine Learning

12. Prospective Study 3 months February to May 2015 Largest gap: non-FPA potential cases Excluded Nevada – distance

13. Prospective Study Rationale Importance Unique challenges Potential outcomes

14. Goals Improve non-FPA potential authorization by 7% Clearer understanding of case circumstances

15. Tracking Staff Assignments/shift Out of region assignments Probabilities of each assignment Noteworthy cases (phone approaches, unplanned mention) Authorization rates (global, individual) Observed/expected donation rate (pre/post study)

16. Next Steps Complementary scheduling Requester scheduled based on potential circumstances Training model

17. Future Study Questions Requester psychosocial impact Family impact Predictive case circumstances? Data on Decision-Makers Case circumstances may change

18. Thank You